Emergency response operations in the maritime sector rely on reliable and proactive systems. This applies to lifeboats for personnel evacuation during accidents. The successful launch of a lifeboat is critical to an effective emergency response at sea. Ensuring that all crew and passengers can safely leave the ship during emergencies is essential. The study focuses on the application of an integrated probabilistic tool for launching failure prediction of an Enclosed Davit-launched lifeboat. The Fault tree is used to build a structural relationship among failure causal factors. The fault tree is mapped to the Bayesian networks (BN) for the overall failure probability prediction. The BN is a machine learning tool that provides a robust method of reasoning, capturing dependencies among failure triggers under uncertainty. The research examined the operational mechanisms and identified potential failure causative factors during response operations. The study found harsh operating conditions, corrosion on the fall wire, a defective engine starter motor, and insufficient lubrication as the most critical factors affecting the enclosed Davit Launch Lifeboat's failure. The study offers a dynamic fault-based structure for managing lifeboat systems to prevent failure during emergencies. The proposed approach offers valuable insights into ensuring system reliability during emergency response operations in the maritime sector.